Sanjay Kulkarni, SVP and Global Head of SAP's Global Delivery Hub, explains how a 12,500-strong services organization translates AI ambition into measurable business value - and why people, not technology, remain the decisive factor in every successful enterprise AI transformation.

What does it actually take to move from AI ambition to measurable enterprise value? In this episode, Sanjay Kulkarni - SVP and Global Head of the Delivery Hub within SAP's Customer Value Group - brings a direct, frontline perspective from leading an organization of 12,500 to 13,000 delivery experts, consultants, architects, and project managers who help customers across every SAP solution and industry turn software investments into concrete business outcomes.
Sanjay opens with a clear diagnosis of where enterprise AI conversations have shifted: from exploratory use cases and proof of concepts to hard questions about scale, governance, and ROI. Customers are no longer asking whether AI is interesting. They are asking how to operationalize it, how to put a hard dollar value on it, and how to make it run responsibly at scale across connected global ecosystems.
Three strategic priorities frame the conversation. First, accelerating time to value - customers expect concrete business outcomes from software, not just successful implementations. Second, embedding AI into core processes in ways that produce tangible, measurable results. Third, using AI to make services delivery itself dramatically more efficient, freeing consultants from repetitive tasks to spend more time solving complex business problems. At the heart of all three, Sanjay returns repeatedly to the same conviction: his top three priorities are people, people, and people.
The General Motors case study illustrates the pattern in action. GM - a long-standing SAP customer managing finance, procurement, supply chain, manufacturing, and HR on SAP - has been embedding Joule and Business AI directly into workflows: helping planners identify supply constraints, supporting procurement sourcing decisions, and enabling finance teams to extract real-time insights from operational data. The goal is not AI as a standalone tool but AI woven into the fabric of how the business runs.
The conversation then turns to what separates organizations that have successfully scaled AI from those still running isolated pilots. Sanjay identifies three consistent markers: a business-outcome-first mindset (not a technology-first one), clean and well-governed data, and genuine organizational transformation - with executive sponsorship, change management, workforce enablement, and a culture of continuous learning treated as non-negotiables.
On the evolving role of SAP consultants, he introduces what he calls a "beautiful trifecta": industry and process knowledge, core product capability, and the technology foundation. All three must come together to shift the consultant's role from functional implementer toward strategic partner who works across lines of business to solve real problems.
The episode closes with a leadership principle that applies well beyond SAP: stop thinking about AI as automation of existing processes, and start rethinking which decisions should remain human, which should be AI-assisted, and which can be fully autonomous. Accountability, Sanjay argues, is not reduced by automation. If anything, it is strengthened.